library(rmarkdown)
library(ggplot2)
library(dplyr)
library(tidyr)
library(Hmisc)

library(summarytools)
library(arsenal)

library(gridExtra)

library(GGally)
library(VIM)

library(kableExtra)
library(pixiedust)

1 Housekeeping

Create a shortcut to the folder where you want your working directory to be. This will be useful later when saving your work.

tmpdir<-"~/Documents/Teaching/Rlabs_Github/CSTAT/"   # set the directory directly

2 Data set

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey examines a nationally representative sample of about 5,000 persons each year. Link to CDC NHANES website

The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. High quality processed activity data combined with mortality and demographic information can be downloaded and used in R with code from Andrew Leroux (https://andrew-leroux.github.io/rnhanesdata/articles).

Here, we look at the subset of participants between 50 and 85 years old from the 2003-2004 and 2005-2006 samples (n=2978) who wore a hip-worn accelerometer in the free living environment for up to 7 days. The response ID is seqn.

2.0.1 Sociodemographic variables

age at examination [years] (i.e. when participants wore the device), age
gender (male and female), gender
race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, and other)
education (< high school graduate, high school graduate/general educational development [GED], some college, and college graduate), educationadult
5 year mortality, NAs for individuals with follow up less than 5 years and alive, yr5.mort
Person Months of Follow-up from MEC/Exam Date, permth.exm (follow-up time in this cohort in years = permth.exm/12)
final mortality status, mortstat

2.0.2 Health and behavior variables

smoking status (current smoker, former smoker [those reporting quitting within the previous 6 months], and nonsmoker), smokecigs
alcohol consumption, drinkstatus
BMI, bmi
obesity, bmi.cat
diabetes, diabetes
congestive heart failure, chf
cancer, cancer
stroke, stroke
average systolic blood pressure using the 4 measurements per participant, sys ,
Total cholesterol (mg/dL), lbxtc
HDL cholesterol, lbdhdd
mobilityproblem (“No Difficulty”,“Any Difficulty”)

2.0.3 Physical activity data

total activity counts per day (TAC/d)
total log activity count (TLAC)
total minutes of moderate/vigorous physical activity (MVPA)
total accelerometer wear time (WT)
sedentary/sleep/non-wear to active transition probability (SATP)
active to sedentary/sleep/non-wear transition probability (ASTP): Bout length was defined as the number of consecutive minutes spent in either an active or sedentary state and a daily activity profile was created for each participant to detect alternating bouts of sedentary and active states. ASTP was defined as the probability of transitioning from an active to a sedentary state and calculated as the reciprocal of the average active bout duration. ASTP was calculated for each day and averaged across valid days to derive a single measure of ASTP for each participant.

In addition, there are total log activity count summary measures (tlac.1, tlac.2, …, tlac.12) in each 2-hr window, i.e. 12AM-2AM, 2AM-4AM, 4AM-6AM, etc.

load(paste0(tmpdir, "nhanesdat.rdata"))
names(nhanesdat) <- make.names(casefold(names(nhanesdat)), allow_=FALSE)

3 Framework for a systematic initial data analysis (IDA)

The main aim of IDA is seen in providing reliable knowledge about the data to enable responsible statistical analyses and interpretation.

This framework was developed for a primary data collection where data are obtained to address a predefined set of research questions, with an elaborated analysis plan. However, IDA is often performed in more complex studies raising additional issues such as an implementation of IDA processes during ongoing data collections to detect data issues while they are potentially remediable.

3.0.1 Intended statistical analysis

Develop a predictive model for 5 year mortality risk in the NHANES cohort using demographic data, comorbidities, lifestyle factors, and physical activity measurements.

In the NHANES example the theoretical research objective could be to find predictors or 5-year mortality.

3.0.2 Six elements of IDA

Meta data
Data cleaning
Data screening
Initial IDA report
Updating analysis plan
IDA report for manuscript

IDA Framework

Metadata setup summarizes background information to properly conduct all following IDA steps. Beyond technical metadata such as labels or plausibility limits, this covers conceptual metadata which combines information from the study protocol, secondary information sources and information about the actual study conduct.

Data cleaning is performed to identify and correct technical data errors. Many errors may not be directly observed and a proper metadata setup is crucial to progress correctly and efficiently in this step

Data screening examines data properties to inform decisions about the realizability of the intended analyses. In contrast to the data cleaning step, the focus is on data properties, not technical errors. However, data screening may reveal structural errors that occurred during the data collection process.

Initial data reporting documents all insights obtained from the previous steps to the research body.

Refining and updating the analysis plan account for findings from the previous IDA steps by making adaptations of the analysis plan.

Reporting IDA in research papers is necessary to ensure transparency regarding key findings and actions in the IDA steps that impacted the analysis or interpretation of results. This reporting step is based on the initial data reporting but clearly focused on the specific paper and what has been done, whereas the former provides a general overview of IDA findings and sugges- tions on ways to handle potential conflicts with the analysis plan.

4 Meta data

Meta data may include the study information, study protocol, and a data dictionary.

library(rnhanesdata)
?rnhanesdata

Add missing labels for physical activity data:

nhanesdat<- nhanesdat %>%
  Hmisc::upData(labels = c(tac = 'total activity counts per day', tlac = 'total log activity count', mvpa='total minutes of moderate/vigorous physical activity', wt ='total accelerometer wear time', satp ='sedentary/sleep/non-wear to active transition probability', astp ='active to sedentary/sleep/non-wear transition probability'))

## Input object size:    1638080 bytes;  80 variables    2978 observations
## New object size: 1486584 bytes;  80 variables    2978 observations

Hmisc::contents(nhanesdat)

## 
## Data frame:nhanesdat 2978 observations and 80 variables    Maximum # NAs:2401
## 
## 
##                                                                        Labels
## seqn                                                                         
## paxcal                                                                       
## paxstat                                                                      
## weekday                                                                      
## sddsrvyr                                                                     
## eligstat                                                                     
## mortstat                                                                     
## permth.exm                                                                   
## permth.int                                                                   
## ucod.leading                                                                 
## diabetes.mcod                                                                
## hyperten.mcod                                                                
## sdmvpsu                                                                      
## sdmvstra                                                                     
## wtint2yr                                                                     
## wtmec2yr                                                                     
## ridagemn                                                                     
## ridageex                                                                     
## ridageyr                                                                     
## bmi                                                                          
## bmi.cat                                                                      
## race                                                                         
## gender                                                                       
## diabetes                                                                     
## chf                                                                          
## chd                                                                          
## cancer                                                                       
## stroke                                                                       
## educationadult                                                               
## mobilityproblem                                                              
## drinkstatus                                                                  
## drinksperweek                                                                
## smokecigs                                                                    
## bpxsy1                                                                       
## bpxsy2                                                                       
## bpxsy3                                                                       
## bpxsy4                                                                       
## lbxtc                                                                        
## lbdhdd                                                                       
## yr5.mort                                                                     
## age                                                                          
## sys                                                                          
## exclude                                                                      
## wtint2yr.unadj                                                               
## wtmec2yr.unadj                                                               
## wtint2yr.unadj.norm                                                          
## wtmec2yr.unadj.norm                                                          
## wtint4yr.unadj                                                               
## wtint4yr.unadj.norm                                                          
## wtmec4yr.unadj                                                               
## wtmec4yr.unadj.norm                                                          
## wtint2yr.adj                                                                 
## wtint2yr.adj.norm                                                            
## wtmec2yr.adj                                                                 
## wtmec2yr.adj.norm                                                            
## wtint4yr.adj                                                                 
## wtint4yr.adj.norm                                                            
## wtmec4yr.adj                                                                 
## wtmec4yr.adj.norm                                                            
## tac                                             total activity counts per day
## tlac                                                 total log activity count
## wt                                              total accelerometer wear time
## st                                                                           
## mvpa                     total minutes of moderate/vigorous physical activity
## about                                                                        
## sbout                                                                        
## satp                sedentary/sleep/non-wear to active transition probability
## astp                active to sedentary/sleep/non-wear transition probability
## tlac.1                                                                       
## tlac.2                                                                       
## tlac.3                                                                       
## tlac.4                                                                       
## tlac.5                                                                       
## tlac.6                                                                       
## tlac.7                                                                       
## tlac.8                                                                       
## tlac.9                                                                       
## tlac.10                                                                      
## tlac.11                                                                      
## tlac.12                                                                      
##                     Levels   Class   Storage  NAs
## seqn                                 integer    0
## paxcal                               integer    0
## paxstat                              integer    0
## weekday                              integer    0
## sddsrvyr                             integer    0
## eligstat                             integer    0
## mortstat                             integer    0
## permth.exm                           integer    0
## permth.int                           integer    0
## ucod.leading                       character 2195
## diabetes.mcod                        integer 2195
## hyperten.mcod                        integer 2195
## sdmvpsu                              integer    0
## sdmvstra                             integer    0
## wtint2yr                              double    0
## wtmec2yr                              double    0
## ridagemn                             integer    0
## ridageex                             integer    0
## ridageyr                             integer    0
## bmi                                   double    0
## bmi.cat                  4           integer    0
## race                     5           integer    0
## gender                   2           integer    0
## diabetes                 2           integer    0
## chf                      2           integer    0
## chd                      2           integer    0
## cancer                   2           integer    0
## stroke                   2           integer    0
## educationadult           3           integer    0
## mobilityproblem          2           integer    0
## drinkstatus              4           integer    0
## drinksperweek                         double   86
## smokecigs                3           integer    0
## bpxsy1                               integer  347
## bpxsy2                               integer  486
## bpxsy3                               integer  523
## bpxsy4                               integer 2401
## lbxtc                                integer    0
## lbdhdd                               integer    0
## yr5.mort                             integer    0
## age                                   double    0
## sys                                  integer    0
## exclude                              integer    0
## wtint2yr.unadj                        double    0
## wtmec2yr.unadj                        double    0
## wtint2yr.unadj.norm                   double    0
## wtmec2yr.unadj.norm                   double    0
## wtint4yr.unadj                        double    0
## wtint4yr.unadj.norm                   double    0
## wtmec4yr.unadj                        double    0
## wtmec4yr.unadj.norm                   double    0
## wtint2yr.adj                          double    0
## wtint2yr.adj.norm                     double    0
## wtmec2yr.adj                          double    0
## wtmec2yr.adj.norm                     double    0
## wtint4yr.adj                          double    0
## wtint4yr.adj.norm                     double    0
## wtmec4yr.adj                          double    0
## wtmec4yr.adj.norm                     double    0
## tac                        numeric    double    0
## tlac                       numeric    double    0
## wt                         numeric    double    0
## st                                    double    0
## mvpa                       numeric    double    0
## about                                 double    0
## sbout                                 double    0
## satp                       numeric    double    0
## astp                       numeric    double    0
## tlac.1                                double    0
## tlac.2                                double    0
## tlac.3                                double    0
## tlac.4                                double    0
## tlac.5                                double    0
## tlac.6                                double    0
## tlac.7                                double    0
## tlac.8                                double    0
## tlac.9                                double    0
## tlac.10                               double    0
## tlac.11                               double    0
## tlac.12                               double    0
## 
## +---------------+----------------------------------------------------------+
## |Variable       |Levels                                                    |
## +---------------+----------------------------------------------------------+
## |bmi.cat        |Normal,Underweight,Overweight,Obese                       |
## +---------------+----------------------------------------------------------+
## |race           |White,Mexican American,Other Hispanic,Black,Other         |
## +---------------+----------------------------------------------------------+
## |gender         |Male,Female                                               |
## +---------------+----------------------------------------------------------+
## |diabetes       |No,Yes                                                    |
## |chf            |                                                          |
## |chd            |                                                          |
## |cancer         |                                                          |
## |stroke         |                                                          |
## +---------------+----------------------------------------------------------+
## |educationadult |Less than high school,High school,More than high school   |
## +---------------+----------------------------------------------------------+
## |mobilityproblem|No Difficulty,Any Difficulty                              |
## +---------------+----------------------------------------------------------+
## |drinkstatus    |Moderate Drinker,Non-Drinker,Heavy Drinker,Missing alcohol|
## +---------------+----------------------------------------------------------+
## |smokecigs      |Never,Former,Current                                      |
## +---------------+----------------------------------------------------------+

cat(Hmisc::html(Hmisc::contents(nhanesdat)), file=file.path(tmpdir, "nhanesdat.html") )

5 Data cleaning

Note the data we use here have already been pre-cleaned.

Steps to achieve this were done as follows.

Exclusions: From 14631 exclude participants who were

younger than 50 years old, or 85 and older at the time they wore the accelerometer (10859 participants);
missing BMI or education predictor variables (41 participants);
had fewer than 3 days of data with at least 10 hours of estimated wear time (517 participants);
missing mortality information (21 participants);
alive with follow up less than 5 years (0 participants);
missing systolic blood pressure, total cholesterol or HDL cholesterol measurements (293 participants).

It is useful to create a flow chart to summarize inclusion/exclusion criteria. Here is a different example from another study that combined data from surveys for two different populations:

STROBE flowdiagram

There are similar flow diagrams for other types of study, e.g. randomized controlled trials (CONSORT) or meta-analyses (PRISMA). These can be found on the website EQUATOR Network.

Recoding

Re-level comorbidities to assign refused/don’t know as not having the condition
Re-level education to have 3 levels and categorize don’t know/refused to be missing
Re-level alcohol consumption to include a level for “missing”
Removed the “bad” days from Act_Analysis and Act_Flags
There is one individual with 501 minutes recorded as NA. These missing data occur on the last day they wore the device for the last 501 minutes of the day. These missing data were imputed with 0.

Set missing ‘drinkstatus’ to NA

indx<-which(nhanesdat$drinkstatus=='Missing alcohol')
nhanesdat$drinkstatus[indx]<-NA

Check data types, labels, and first few values of each of the variables needed for the analysis:

vars<- c( "yr5.mort" , "permth.exm", "bmi" , "bmi.cat", "race" ,"gender",  "diabetes", "chf" ,"chd" , "cancer", "stroke" , "educationadult" , "mobilityproblem", "drinkstatus" , "smokecigs",   "age"  ,"sys" ,"lbxtc", "lbdhdd","tac", "tlac","wt" , "mvpa", "satp", "astp", "bpxsy1", "bpxsy2","bpxsy3","bpxsy4", "drinksperweek")

str(nhanesdat[,vars])

## 'data.frame':    2978 obs. of  30 variables:
##  $ yr5.mort       : int  0 0 0 1 0 0 0 0 0 0 ...
##  $ permth.exm     : int  135 149 127 24 153 154 152 150 112 151 ...
##  $ bmi            : num  31.3 25.5 19.6 28.3 38 ...
##  $ bmi.cat        : Factor w/ 4 levels "Normal","Underweight",..: 4 3 1 3 4 1 4 4 3 4 ...
##  $ race           : Factor w/ 5 levels "White","Mexican American",..: 1 1 4 1 2 4 4 1 1 1 ...
##  $ gender         : Factor w/ 2 levels "Male","Female": 1 2 1 1 2 2 2 2 1 1 ...
##  $ diabetes       : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ chf            : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 2 1 ...
##  $ chd            : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 1 1 1 2 1 ...
##  $ cancer         : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 2 1 1 1 ...
##  $ stroke         : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ educationadult : Factor w/ 3 levels "Less than high school",..: 2 3 2 3 1 1 2 2 2 2 ...
##  $ mobilityproblem: Factor w/ 2 levels "No Difficulty",..: 1 1 2 2 1 1 1 2 2 1 ...
##  $ drinkstatus    : Factor w/ 4 levels "Moderate Drinker",..: 2 3 NA 2 2 2 2 2 2 2 ...
##  $ smokecigs      : Factor w/ 3 levels "Never","Former",..: 1 3 3 2 2 1 1 2 2 2 ...
##  $ age            : num  56 52.8 63.8 83.9 50.6 ...
##  $ sys            : int  120 133 123 154 115 131 152 116 119 127 ...
##  $ lbxtc          : int  254 174 191 141 173 230 195 156 152 177 ...
##  $ lbdhdd         : int  37 119 92 34 45 51 53 47 30 59 ...
##  $ tac            : 'labelled' num  409353 286408 130778 102563 511161 ...
##   ..- attr(*, "label")= chr "total activity counts per day"
##  $ tlac           : 'labelled' num  3522 3335 2749 2104 4390 ...
##   ..- attr(*, "label")= chr "total log activity count"
##  $ wt             : 'labelled' num  900 783 1053 813 1014 ...
##   ..- attr(*, "label")= chr "total accelerometer wear time"
##  $ mvpa           : 'labelled' num  48.29 9.43 4.71 3 40.5 ...
##   ..- attr(*, "label")= chr "total minutes of moderate/vigorous physical activity"
##  $ satp           : 'labelled' num  0.0982 0.0885 0.0909 0.0724 0.1229 ...
##   ..- attr(*, "label")= chr "sedentary/sleep/non-wear to active transition probability"
##  $ astp           : 'labelled' num  0.232 0.215 0.405 0.335 0.177 ...
##   ..- attr(*, "label")= chr "active to sedentary/sleep/non-wear transition probability"
##  $ bpxsy1         : int  124 128 126 154 118 130 164 124 124 132 ...
##  $ bpxsy2         : int  124 138 120 NA 110 NA 144 114 116 126 ...
##  $ bpxsy3         : int  112 134 122 NA 116 132 148 110 118 124 ...
##  $ bpxsy4         : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ drinksperweek  : num  0 28 NA 0 0 0 0 0 0 0 ...

x<-round(mean(nhanesdat$tlac), 2)

A quick summary of the variables can be done by using summary(nhanesdat[,vars]) of more elaborate with the package summarytools.

The mean of the total log activity counts is 2758.53.

#library(summarytools)
print(dfSummary(nhanesdat[,vars[-c(1,2)]]), method = 'render')

Data Frame Summary

Dimensions: 2978 x 28
Duplicates: 0

No	Variable	Label	Stats / Values	Freqs (% of Valid)	Valid	Missing
1	bmi [numeric]		mean (sd) : 28.8 (5.8) min < med < max : 13.36 < 28.01 < 57.41 IQR (CV) : 7.1 (0.2)	1551 distinct values	2978 (100%)	0 (0%)
2	bmi.cat [factor]		1. Normal 2. Underweight 3. Overweight 4. Obese	760 (25.5%) 29 (1.0%) 1150 (38.6%) 1039 (34.9%)	2978 (100%)	0 (0%)
3	race [factor]		1. White 2. Mexican American 3. Other Hispanic 4. Black 5. Other	1756 (59.0%) 538 (18.1%) 56 (1.9%) 534 (17.9%) 94 (3.2%)	2978 (100%)	0 (0%)
4	gender [factor]		1. Male 2. Female	1523 (51.1%) 1455 (48.9%)	2978 (100%)	0 (0%)
5	diabetes [factor]		1. No 2. Yes	2460 (82.6%) 518 (17.4%)	2978 (100%)	0 (0%)
6	chf [factor]		1. No 2. Yes	2810 (94.4%) 168 (5.6%)	2978 (100%)	0 (0%)
7	chd [factor]		1. No 2. Yes	2734 (91.8%) 244 (8.2%)	2978 (100%)	0 (0%)
8	cancer [factor]		1. No 2. Yes	2523 (84.7%) 455 (15.3%)	2978 (100%)	0 (0%)
9	stroke [factor]		1. No 2. Yes	2804 (94.2%) 174 (5.8%)	2978 (100%)	0 (0%)
10	educationadult [factor]		1. Less than high school 2. High school 3. More than high school	945 (31.7%) 739 (24.8%) 1294 (43.5%)	2978 (100%)	0 (0%)
11	mobilityproblem [factor]		1. No Difficulty 2. Any Difficulty	2038 (68.4%) 940 (31.6%)	2978 (100%)	0 (0%)
12	drinkstatus [factor]		1. Moderate Drinker 2. Non-Drinker 3. Heavy Drinker 4. Missing alcohol	1445 (50.0%) 1266 (43.8%) 181 (6.3%) 0 (0.0%)	2892 (97.11%)	86 (2.89%)
13	smokecigs [factor]		1. Never 2. Former 3. Current	1326 (44.5%) 1147 (38.5%) 505 (17.0%)	2978 (100%)	0 (0%)
14	age [numeric]		mean (sd) : 65.85 (9.62) min < med < max : 50 < 65.42 < 84.92 IQR (CV) : 15.25 (0.15)	417 distinct values	2978 (100%)	0 (0%)
15	sys [integer]		mean (sd) : 133.56 (21.36) min < med < max : 73 < 131 < 270 IQR (CV) : 26 (0.16)	131 distinct values	2978 (100%)	0 (0%)
16	lbxtc [integer]		mean (sd) : 205.03 (42.3) min < med < max : 82 < 203 < 458 IQR (CV) : 55 (0.21)	239 distinct values	2978 (100%)	0 (0%)
17	lbdhdd [integer]		mean (sd) : 55.31 (16.36) min < med < max : 17 < 53 < 188 IQR (CV) : 21 (0.3)	98 distinct values	2978 (100%)	0 (0%)
18	tac [labelled, numeric]	total activity counts per day	mean (sd) : 209786.13 (112910.97) min < med < max : 8931.83 < 193949.91 < 912075.6 IQR (CV) : 145388.35 (0.54)	2975 distinct values	2978 (100%)	0 (0%)
19	tlac [labelled, numeric]	total log activity count	mean (sd) : 2758.53 (727.2) min < med < max : 429.93 < 2754.01 < 5655.47 IQR (CV) : 978.6 (0.26)	2976 distinct values	2978 (100%)	0 (0%)
20	wt [labelled, numeric]	total accelerometer wear time	mean (sd) : 878.53 (138.51) min < med < max : 615.33 < 860.69 < 1440 IQR (CV) : 137.8 (0.16)	2215 distinct values	2978 (100%)	0 (0%)
21	mvpa [labelled, numeric]	total minutes of moderate/vigorous physical activity	mean (sd) : 13.85 (17.05) min < med < max : 0 < 7.43 < 152.33 IQR (CV) : 16.79 (1.23)	734 distinct values	2978 (100%)	0 (0%)
22	satp [labelled, numeric]	sedentary/sleep/non-wear to active transition probability	mean (sd) : 0.08 (0.02) min < med < max : 0.01 < 0.08 < 0.2 IQR (CV) : 0.03 (0.27)	2976 distinct values	2978 (100%)	0 (0%)
23	astp [labelled, numeric]	active to sedentary/sleep/non-wear transition probability	mean (sd) : 0.3 (0.09) min < med < max : 0.05 < 0.29 < 0.74 IQR (CV) : 0.11 (0.3)	2976 distinct values	2978 (100%)	0 (0%)
24	bpxsy1 [integer]		mean (sd) : 134.92 (22.08) min < med < max : 74 < 132 < 270 IQR (CV) : 26 (0.16)	73 distinct values	2631 (88.35%)	347 (11.65%)
25	bpxsy2 [integer]		mean (sd) : 132.93 (21.05) min < med < max : 72 < 132 < 226 IQR (CV) : 26 (0.16)	71 distinct values	2492 (83.68%)	486 (16.32%)
26	bpxsy3 [integer]		mean (sd) : 131.43 (20.76) min < med < max : 80 < 130 < 222 IQR (CV) : 26 (0.16)	69 distinct values	2455 (82.44%)	523 (17.56%)
27	bpxsy4 [integer]		mean (sd) : 132.89 (20.02) min < med < max : 80 < 130 < 204 IQR (CV) : 24 (0.15)	55 distinct values	577 (19.38%)	2401 (80.62%)
28	drinksperweek [numeric]		mean (sd) : 2.66 (6.7) min < med < max : 0 < 0.06 < 112 IQR (CV) : 2 (2.52)	99 distinct values	2892 (97.11%)	86 (2.89%)

Generated by summarytools 0.8.9 (R version 3.6.1)
2020-04-11

The outcome variable is 5 year mortality.

#library(summarytools)
print(dfSummary(nhanesdat[,vars[c(1,2)]]), method = 'render')

Data Frame Summary

Dimensions: 2978 x 2
Duplicates: 2821

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	yr5.mort [integer]	mean (sd) : 0.1 (0.3) min < med < max : 0 < 0 < 1 mode:	0 : 2681 (90.0%) 1 : 297 (10.0%)		2978 (100%)	0 (0%)
2	permth.exm [integer]	mean (sd) : 116.33 (33.52) min < med < max : 1 < 126 < 157 IQR (CV) : 27 (0.29)	157 distinct values		2978 (100%)	0 (0%)

Generated by summarytools 0.8.9 (R version 3.6.1)
2020-04-11

5.0.1 Univariate distributions for continuous variables

#library(gridExtra)

g1<- ggplot(data = nhanesdat, aes(age))+geom_histogram(bins = 600, color = "black", fill = "grey")
g2<- ggplot(data = nhanesdat, aes(bmi))+geom_histogram(bins = 600, color = "black", fill = "grey")
g3<- ggplot(data = nhanesdat, aes(permth.exm))+geom_histogram(bins = 600, color = "black", fill = "grey")
g4<- ggplot(data = nhanesdat, aes(tac))+geom_histogram(bins = 600, color = "black", fill = "grey")
g5<- ggplot(data = nhanesdat, aes(mvpa))+geom_histogram(bins = 600, color = "black", fill = "grey")
g6<- ggplot(data = nhanesdat, aes(astp))+geom_histogram(bins = 600, color = "black", fill = "grey")
g7<- ggplot(data = nhanesdat, aes(satp))+geom_histogram(bins = 600, color = "black", fill = "grey")
g8<- ggplot(data = nhanesdat, aes(lbxtc))+geom_histogram(bins = 600, color = "black", fill = "grey")
g9<- ggplot(data = nhanesdat, aes(bpxsy4))+geom_histogram(bins = 600, color = "black", fill = "grey")


grid.arrange(g1, g2,g3,g4,g5,g6,g7,g8,g9, nrow = 3)

If there is a marathon runner, what would the TAC be?

Unrealistic high or low values may be detected by histograms or by calculating minimum and maximum values. If a variable ‘Chemo’ is expected to have 0-1 values (non treated or treated with chemo) and instead we see numbers 4,2,7, etc, this means that column labels may have been swapped. If we see a year of birth of 2078, this is likely 1978 instead. If the value can be checked or is obvious, it can be corrected, sometimes it is necessary to set the value to missing instead. It is good to check dates and length of time, for example subtracting the follow-up date from the data of study entry or the data of birth to make sure there are no negative time differences.

5.0.2 Univariate distributions for categorical variables

#library(gridExtra)

#  group = chemo, fill = chemo
g1 <-nhanesdat %>% ggplot(aes(x = drinkstatus)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() + theme(axis.text.x = element_text(angle = 60));
g2 <-nhanesdat %>% ggplot(aes(x = race)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() + theme(axis.text.x = element_text(angle = 60)); 
g3 <-nhanesdat %>% ggplot(aes(x = educationadult)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() + theme(axis.text.x = element_text(angle = 60));
g4 <-nhanesdat %>% ggplot(aes(x = mobilityproblem)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() ;
g5 <-nhanesdat %>% ggplot(aes(x = bmi.cat)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() 
g6 <-nhanesdat %>% ggplot(aes(x = gender)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() ;
g7 <-nhanesdat %>% ggplot(aes(x = cancer)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() ;
g8 <-nhanesdat %>% ggplot(aes(x = chf)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() ;
g9 <-nhanesdat %>% ggplot(aes(x = stroke)) +  geom_bar(position = position_dodge(), fill="dark grey") +theme_bw() ;


grid.arrange(g1, g2,g3,g4,g5,g6,g7,g8,g9, nrow = 3)

5.0.3 Interactive graphics

library(plotly)
fig <- plot_ly(nhanesdat, x = ~bmi,  color = ~gender, type="box")
fig

5.0.4 Pairwise associations

Visualizing a correlation matrix between physical activity variables and age

corvars<-c("age", "tac", "tlac", "wt", "mvpa", "satp", "astp")
cor(nhanesdat[,corvars])

##             age        tac       tlac          wt       mvpa       satp
## age   1.0000000 -0.4316314 -0.3842809 -0.01991550 -0.3259843 -0.3331776
## tac  -0.4316314  1.0000000  0.8127898  0.17090389  0.8414233  0.4776727
## tlac -0.3842809  0.8127898  1.0000000  0.34284265  0.4773299  0.8103376
## wt   -0.0199155  0.1709039  0.3428426  1.00000000  0.1042871  0.4643814
## mvpa -0.3259843  0.8414233  0.4773299  0.10428711  1.0000000  0.2114295
## satp -0.3331776  0.4776727  0.8103376  0.46438139  0.2114295  1.0000000
## astp  0.2976469 -0.7547711 -0.7212789  0.05170059 -0.4553590 -0.3197902
##             astp
## age   0.29764689
## tac  -0.75477108
## tlac -0.72127891
## wt    0.05170059
## mvpa -0.45535905
## satp -0.31979021
## astp  1.00000000

#library(GGally)
ggcorr(nhanesdat[,corvars])

The highest correlations are between the pairs (tac, tlac), (mvpa, tac), and (satp,tlac).

Associations can be summarized in a scatter plot with “trend lines.”

g1<-ggplot(nhanesdat, aes(age, tac)) +
  geom_point() +
  geom_smooth(method=lm, se=FALSE)

g2<-ggplot(nhanesdat, aes(astp, tac)) +
  geom_point() +
  geom_smooth()

g3<-ggplot(nhanesdat, aes(mvpa, tac)) +
  geom_point() +
  geom_smooth()

g4<-ggplot(nhanesdat, aes(age, astp)) +
  geom_point() +
  geom_smooth()

g5<-ggplot(nhanesdat, aes(satp, wt)) +
  geom_point() +
  geom_smooth()

g6<-ggplot(nhanesdat, aes(satp, astp)) +
  geom_point() +
  geom_smooth()

grid.arrange(g1, g2,g3, g4, g5, g6,  nrow = 2)

# geom_smooth() default method = 'loess' and formula 'y ~ x'

Visualization of other types of assocations:

#library(GGally)
ggpairs(nhanesdat[,c("age", "gender", "bmi", "tac")])

Unrealistic combinations of values can be detected by scatter plots or cross tables.

6 Data screening

Some of the quantitative or graphical summaries are identical to the ones in the data cleaning step. However, now the focus is on data properties and potential impact on the intended statistical analysis plan.

Review data summaries from the previous step with this new purpose in mind.

Examples of what data screening might reveal:

If we observe an uneven distribution of age and most of the survey respondents turn out to be around 50-60 years old, this would reveal that older participants are not well represented.
Health behaviors may differ between different age groups, thus there would be a question of selection bias and generalizability.

summary(nhanesdat$bpxsy4)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    80.0   120.0   130.0   132.9   144.0   204.0    2401

prop.table(table(is.na(nhanesdat$bpxsy4)))

## 
##     FALSE      TRUE 
## 0.1937542 0.8062458

Here we see that 80% of the 4th measurement of the systolic blood pressure is missing.

6.0.1 Missing data patterns

# library(VIM)
aggr(nhanesdat[,vars],numbers=TRUE)

There does not seem to be a pattern of missingness.

There may be situations where answers to question are missing depending on education level which would point to a systematic pattern.

We can check differences in variables stratified by education level, if this could have an impact on the intended analysis plan.

hanesedu<-tableby(educationadult ~ ., data=nhanesdat[,vars], test=FALSE, numeric.stats=c("median","q1q3", "range"))

# need results='asis' above for this to look good in the output
summary(hanesedu, title = "Participant characteristics by education level")

Participant characteristics by education level
	Less than high school (N=945)	High school (N=739)	More than high school (N=1294)	Total (N=2978)
yr5.mort
Median	0.000	0.000	0.000	0.000
Q1, Q3	0.000, 0.000	0.000, 0.000	0.000, 0.000	0.000, 0.000
Range	0.000 - 1.000	0.000 - 1.000	0.000 - 1.000	0.000 - 1.000
permth.exm
Median	122.000	125.000	127.000	126.000
Q1, Q3	101.000, 137.000	111.000, 137.000	115.000, 139.000	111.000, 138.000
Range	2.000 - 156.000	1.000 - 157.000	2.000 - 157.000	1.000 - 157.000
bmi
Median	27.800	28.400	27.885	28.005
Q1, Q3	24.850, 31.980	24.895, 32.225	24.678, 31.758	24.793, 31.890
Range	15.920 - 53.540	14.700 - 50.750	13.360 - 57.410	13.360 - 57.410
bmi.cat
Normal	239 (25.3%)	182 (24.6%)	339 (26.2%)	760 (25.5%)
Underweight	7 (0.7%)	8 (1.1%)	14 (1.1%)	29 (1.0%)
Overweight	366 (38.7%)	270 (36.5%)	514 (39.7%)	1150 (38.6%)
Obese	333 (35.2%)	279 (37.8%)	427 (33.0%)	1039 (34.9%)
race
White	335 (35.4%)	509 (68.9%)	912 (70.5%)	1756 (59.0%)
Mexican American	366 (38.7%)	81 (11.0%)	91 (7.0%)	538 (18.1%)
Other Hispanic	30 (3.2%)	7 (0.9%)	19 (1.5%)	56 (1.9%)
Black	195 (20.6%)	121 (16.4%)	218 (16.8%)	534 (17.9%)
Other	19 (2.0%)	21 (2.8%)	54 (4.2%)	94 (3.2%)
gender
Male	495 (52.4%)	350 (47.4%)	678 (52.4%)	1523 (51.1%)
Female	450 (47.6%)	389 (52.6%)	616 (47.6%)	1455 (48.9%)
diabetes
No	733 (77.6%)	609 (82.4%)	1118 (86.4%)	2460 (82.6%)
Yes	212 (22.4%)	130 (17.6%)	176 (13.6%)	518 (17.4%)
chf
No	874 (92.5%)	697 (94.3%)	1239 (95.7%)	2810 (94.4%)
Yes	71 (7.5%)	42 (5.7%)	55 (4.3%)	168 (5.6%)
chd
No	863 (91.3%)	672 (90.9%)	1199 (92.7%)	2734 (91.8%)
Yes	82 (8.7%)	67 (9.1%)	95 (7.3%)	244 (8.2%)
cancer
No	831 (87.9%)	623 (84.3%)	1069 (82.6%)	2523 (84.7%)
Yes	114 (12.1%)	116 (15.7%)	225 (17.4%)	455 (15.3%)
stroke
No	872 (92.3%)	696 (94.2%)	1236 (95.5%)	2804 (94.2%)
Yes	73 (7.7%)	43 (5.8%)	58 (4.5%)	174 (5.8%)
mobilityproblem
No Difficulty	556 (58.8%)	480 (65.0%)	1002 (77.4%)	2038 (68.4%)
Any Difficulty	389 (41.2%)	259 (35.0%)	292 (22.6%)	940 (31.6%)
drinkstatus
N-Miss	37	24	25	86
Moderate Drinker	353 (38.9%)	354 (49.5%)	738 (58.2%)	1445 (50.0%)
Non-Drinker	507 (55.8%)	316 (44.2%)	443 (34.9%)	1266 (43.8%)
Heavy Drinker	48 (5.3%)	45 (6.3%)	88 (6.9%)	181 (6.3%)
Missing alcohol	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
smokecigs
Never	398 (42.1%)	333 (45.1%)	595 (46.0%)	1326 (44.5%)
Former	347 (36.7%)	275 (37.2%)	525 (40.6%)	1147 (38.5%)
Current	200 (21.2%)	131 (17.7%)	174 (13.4%)	505 (17.0%)
age
Median	68.167	66.250	62.667	65.417
Q1, Q3	61.250, 75.667	59.083, 73.500	55.667, 70.917	58.083, 73.333
Range	50.000 - 84.917	50.000 - 84.833	50.000 - 84.917	50.000 - 84.917
sys
Median	134.000	132.000	129.000	131.000
Q1, Q3	122.000, 149.000	119.000, 145.000	117.000, 142.000	119.000, 145.000
Range	80.000 - 270.000	73.000 - 217.000	85.000 - 215.000	73.000 - 270.000
lbxtc
Median	204.000	199.000	205.000	203.000
Q1, Q3	172.000, 231.000	174.000, 228.000	178.000, 229.750	175.000, 230.000
Range	102.000 - 458.000	82.000 - 427.000	98.000 - 367.000	82.000 - 458.000
lbdhdd
Median	52.000	52.000	54.000	53.000
Q1, Q3	42.000, 62.000	43.000, 64.000	44.000, 67.000	43.000, 64.000
Range	23.000 - 188.000	24.000 - 122.000	17.000 - 154.000	17.000 - 188.000
total activity counts per day
Median	177720.857	186759.714	206617.929	193949.914
Q1, Q3	113655.429, 272434.000	123946.871, 253500.643	138213.613, 278977.357	126602.250, 271990.600
Range	26157.714 - 912075.600	20141.333 - 825133.333	8931.833 - 784031.429	8931.833 - 912075.600
total log activity count
Median	2746.781	2721.324	2770.573	2754.012
Q1, Q3	2191.198, 3272.205	2257.260, 3215.428	2316.710, 3246.734	2262.797, 3241.398
Range	661.230 - 5655.468	779.557 - 5322.091	429.929 - 5078.713	429.929 - 5655.468
total accelerometer wear time
Median	837.143	857.857	875.310	860.690
Q1, Q3	771.333, 915.714	792.357, 929.143	812.607, 937.125	793.232, 931.036
Range	615.333 - 1440.000	627.000 - 1440.000	615.333 - 1440.000	615.333 - 1440.000
total minutes of moderate/vigorous physical activity
Median	5.667	5.800	9.633	7.429
Q1, Q3	2.000, 16.286	2.071, 16.143	3.333, 22.714	2.500, 19.286
Range	0.000 - 136.600	0.000 - 152.333	0.000 - 122.286	0.000 - 152.333
sedentary/sleep/non-wear to active transition probability
Median	0.080	0.081	0.082	0.081
Q1, Q3	0.067, 0.095	0.066, 0.096	0.068, 0.097	0.067, 0.096
Range	0.018 - 0.199	0.023 - 0.169	0.008 - 0.197	0.008 - 0.199
active to sedentary/sleep/non-wear transition probability
Median	0.287	0.292	0.288	0.289
Q1, Q3	0.231, 0.353	0.240, 0.346	0.245, 0.339	0.240, 0.345
Range	0.073 - 0.690	0.094 - 0.745	0.052 - 0.728	0.052 - 0.745
bpxsy1
Median	136.000	134.000	130.000	132.000
Q1, Q3	124.000, 152.000	120.000, 146.000	118.000, 143.000	120.000, 146.000
Range	80.000 - 270.000	74.000 - 212.000	86.000 - 228.000	74.000 - 270.000
bpxsy2
Median	134.000	132.000	128.000	132.000
Q1, Q3	122.000, 150.000	118.000, 144.000	116.000, 140.000	118.000, 144.000
Range	80.000 - 226.000	72.000 - 218.000	82.000 - 204.000	72.000 - 226.000
bpxsy3
Median	132.000	130.000	126.000	130.000
Q1, Q3	120.000, 146.000	117.500, 144.000	116.000, 140.000	118.000, 144.000
Range	88.000 - 222.000	80.000 - 216.000	84.000 - 196.000	80.000 - 222.000
bpxsy4
Median	132.000	131.000	130.000	130.000
Q1, Q3	120.000, 144.000	118.500, 144.000	118.000, 144.000	120.000, 144.000
Range	86.000 - 204.000	80.000 - 188.000	90.000 - 202.000	80.000 - 204.000
drinksperweek
Median	0.000	0.038	0.230	0.058
Q1, Q3	0.000, 0.467	0.000, 1.630	0.000, 3.000	0.000, 2.000
Range	0.000 - 63.000	0.000 - 112.000	0.000 - 70.000	0.000 - 112.000

Or check specific item only (total log activity count)

nhanesdat %>%
  group_by(educationadult) %>%
  dplyr::summarise(mean(tlac, na.rm=TRUE))

## # A tibble: 3 x 2
##   educationadult        `mean(tlac, na.rm = TRUE)`
##   <fct>                                      <dbl>
## 1 Less than high school                      2755.
## 2 High school                                2735.
## 3 More than high school                      2774.

7 Initial data reporting

An initial data report would be, for example, an R markdown file that documents all insights obtained from the previous steps to inform collaborators.

7.0.1 Creating tables

Here are two R packages with nice output for tables:

tmp<-nhanesdat[,c("age", "gender", "race", "educationadult", "astp",  "satp")]

# library(kableExtra)

kable(tmp[1:10,], format="html", align = "c") %>%
  kable_styling(c( "striped", "bordered")) %>%
  row_spec(3, bold = T, color = "red", background = "yellow") %>%
  footnote(general = "These are the first 10 participants.", general_title = "Note: ")

age	gender	race	educationadult	astp	satp
56.00000	Male	White	High school	0.2315452	0.09819184
52.83333	Female	White	More than high school	0.2147554	0.08849793
63.83333	Male	Black	High school	0.4045712	0.09094480
83.91667	Male	White	More than high school	0.3353814	0.07236747
50.58333	Female	Mexican American	Less than high school	0.1767232	0.12290906
55.58333	Female	Black	Less than high school	0.3031173	0.09761710
57.33333	Female	Black	High school	0.2240179	0.08643943
84.25000	Female	White	High school	0.4687016	0.07410545
69.00000	Male	White	High school	0.3659029	0.05526371
55.91667	Male	White	High school	0.2614186	0.11077929
Note:
These are the first 10 participants.

# library(pixiedust)
fit<-lm(tac ~ gender+ age + mvpa + satp , data=nhanesdat)

dust(fit, caption="Association with Total activity count", align="center") %>%
  sprinkle(cols = c("estimate", "std.error", "statistic"), round = 3) %>%
  sprinkle_colnames(term = "Term", std.error = "SE", statistic = "T-statistic",
                    estimate = "Coefficient", p.value = "P-value") %>%
  sprinkle(rows = 3, cols = 1:5,  bg = "yellow",
           border = c("left", "right", "top", "bottom"),
           border_color = "blue") %>% 
  sprinkle_print_method("html")

Table 1: Association with Total activity count
Term	Coefficient	SE	T-statistic	P-value
(Intercept)	95942.577	8809.055	10.891	0
genderFemale	-6752.666	1868.96	-3.613	0.0003076
age	-1087.987	103.912	-10.47	0
mvpa	4923.248	57.987	84.903	0
satp	1463022.973	43067.328	33.971	0

8 Refining/Updating the analysis plan

The research questions and statistical analysis plan are established before the initial data analysis. The data screening steps might reveal that the originally planned statistical model is not feasible.

There are a number of reasons for changing a statistical analysis plan.

Additional exclusion of variables or participants with too many missing values (to be noted in the flow diagram). Or use of imputation methods.
Data transformation: The distribution of categorical variable is highly uneven which may lead to combining groups. Example: A small proportion is underweight.

prop.table(table(nhanesdat$bmi.cat))

## 
##      Normal Underweight  Overweight       Obese 
## 0.255204835 0.009738079 0.386165212 0.348891874

Example: Comorbidities can have too few events. If the effect on the outcome is similar, it may be possible to combine several.

table(nhanesdat$stroke, exclude = NULL);

## 
##   No  Yes 
## 2804  174

prop.table(table(nhanesdat$stroke, exclude = NULL))

## 
##         No        Yes 
## 0.94157152 0.05842848

nhanesdat$cvs<-ifelse(nhanesdat$chf=="Yes" | nhanesdat$stroke=="Yes", "Yes","No" )

table(nhanesdat$cvs, exclude = NULL);

## 
##   No  Yes 
## 2669  309

prop.table(table(nhanesdat$cvs, exclude = NULL))

## 
##        No       Yes 
## 0.8962391 0.1037609

Data transformation: The distributions of a continuous variable has spikes at some values which may lead to categorization. Note, it may not be necessary to transform skewed data to normally distributed data for regression modeling, but a log transform can de-emphasize the effect of outliers.

g10<-ggplot(nhanesdat, aes(satp, tac)) +
  geom_point() +
  geom_smooth()

g10+ facet_wrap(~gender)

g11<-ggplot(nhanesdat, aes(satp, tlac)) +
  geom_point() +
  geom_smooth()

g11+ facet_wrap(~gender)

Exclusion or transformation: The distribution of a continuous variable is unexpectedly bimodal (possibly indicating an error with the data collection instrument). Variables are highly correlated.
Unexpected confounding: additional variables may be added to the model
Unexpected population heterogeneity: expand inclusion/exclusion criteria

Alternatives:

Take functional forms into account in any regression modeling and conduct careful model diagnostics to take into account such findings.
Expand the original statistical analysis plan and add sensitivity analyses.

Sometimes we discover during the IDA process that the original research question can not be answered. Sometimes we discover that no further modeling is required and that an Initial Data Analysis (descriptive summary) is enough for the manuscript.

9 Reporting IDA findings in the final report/manuscript

IDA methodology to be described in Methods;
IDA results to be described in Methods or Results;
Impact of IDA on interpretation to be described in discussion.
Pre-planned decisions should be reported in Methods;
IDA driven alterations of the analysis plan should be reported with motivation in Methods.
Participants’ characteristics should be checked for consistency with expectations and for potential impact on analysis and interpretation. At a minimum a statement should be included to confirm no violated expectations.
Full reporting of missingness, e.g. a flow chart describes unit missingness and a table for item missingness of variables.
Associations not involving the research question but with potential impact on interpretation of results should be reported.

10 Session info

sessionInfo()

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] plotly_4.9.0       pixiedust_0.8.6    kableExtra_1.1.0   VIM_4.8.0         
##  [5] data.table_1.12.8  colorspace_1.4-1   GGally_1.4.0       gridExtra_2.3     
##  [9] arsenal_3.3.0      summarytools_0.8.9 Hmisc_4.2-0        Formula_1.2-3     
## [13] survival_2.44-1.1  lattice_0.20-38    tidyr_1.0.0        dplyr_0.8.5       
## [17] ggplot2_3.3.0      rmarkdown_1.15    
## 
## loaded via a namespace (and not attached):
##   [1] pryr_0.1.4          ellipsis_0.3.0      class_7.3-15       
##   [4] rio_0.5.16          htmlTable_1.13.2    base64enc_0.1-3    
##   [7] rstudioapi_0.11     farver_2.0.3        fansi_0.4.1        
##  [10] lubridate_1.7.4     ranger_0.11.2       xml2_1.2.2         
##  [13] codetools_0.2-16    splines_3.6.1       robustbase_0.93-5  
##  [16] knitr_1.28          jsonlite_1.6        broom_0.5.2        
##  [19] cluster_2.1.0       shiny_1.3.2         readr_1.3.1        
##  [22] compiler_3.6.1      httr_1.4.1          backports_1.1.5    
##  [25] assertthat_0.2.1    Matrix_1.2-17       lazyeval_0.2.2     
##  [28] cli_2.0.2           later_0.8.0         acepack_1.4.1      
##  [31] htmltools_0.3.6     tools_3.6.1         gtable_0.3.0       
##  [34] glue_1.3.2          Rcpp_1.0.4          carData_3.0-2      
##  [37] cellranger_1.1.0    vctrs_0.2.4         nlme_3.1-140       
##  [40] crosstalk_1.0.0     lmtest_0.9-37       xfun_0.12          
##  [43] laeken_0.5.0        stringr_1.4.0       openxlsx_4.1.0.1   
##  [46] rvest_0.3.4         mime_0.9            lifecycle_0.2.0    
##  [49] DEoptimR_1.0-8      MASS_7.3-51.4       zoo_1.8-6          
##  [52] scales_1.1.0        hms_0.5.3           promises_1.0.1     
##  [55] RColorBrewer_1.1-2  yaml_2.2.1          curl_4.3           
##  [58] pander_0.6.3        rpart_4.1-15        reshape_0.8.8      
##  [61] latticeExtra_0.6-28 stringi_1.4.6       highr_0.8          
##  [64] e1071_1.7-2         checkmate_1.9.4     boot_1.3-22        
##  [67] zip_2.0.4           rlang_0.4.5         pkgconfig_2.0.3    
##  [70] matrixStats_0.55.0  bitops_1.0-6        evaluate_0.14      
##  [73] purrr_0.3.3         rapportools_1.0     htmlwidgets_1.3    
##  [76] labeling_0.3        tidyselect_1.0.0    plyr_1.8.4         
##  [79] magrittr_1.5        R6_2.4.1            generics_0.0.2     
##  [82] pillar_1.4.3        haven_2.2.0         foreign_0.8-71     
##  [85] withr_2.1.2         mgcv_1.8-28         abind_1.4-5        
##  [88] RCurl_1.95-4.12     sp_1.3-1            nnet_7.3-12        
##  [91] tibble_2.1.3        crayon_1.3.4        car_3.0-3          
##  [94] utf8_1.1.4          readxl_1.3.1        forcats_0.5.0      
##  [97] vcd_1.4-4           digest_0.6.25       webshot_0.5.1      
## [100] xtable_1.8-4        httpuv_1.5.2        munsell_0.5.0      
## [103] viridisLite_0.3.0

11 References

Schmidt CO, Vach W, le Cessie S, Huebner M. STRATOS: Introducing the Initial Data Analysis Topic Group (TG3). Biometric Bulletin 2018; 35 (2): 10-11. This module uses text excerpts from this short introduction.
Huebner M, le Cessie S, Schmidt CO, Vach W . A contemporary conceptual framework for initial data analysis. Observational Studies 2018; 4: 171-192. Link This is the main paper that introduces the initial data analysis framework and explains concepts.
Huebner M, Vach W, le Cessie S, Schmidt CO, Lusa L. Hidden Analyses: a review of reporting practice and recommendations for more transparent reporting of initial data analyses. BMC Med Res Meth 2020; 20:61. Link This paper reviews reporting practices and has recommendations for more transparency.
Leek JT and Peng RD. (2015). Statistics: P values are just the tip of the iceberg. Nature, 520(7549):612. Link

CSTAT thanks Marianne Huebner for the development of this module.↩

Initial Data Analysis

Center for Statistical Training and Consulting¹

11 April 2020

1 Housekeeping

2 Data set

2.0.1 Sociodemographic variables

2.0.2 Health and behavior variables

2.0.3 Physical activity data

3 Framework for a systematic initial data analysis (IDA)

3.0.1 Intended statistical analysis

3.0.2 Six elements of IDA

4 Meta data

5 Data cleaning

Data Frame Summary

Data Frame Summary

5.0.1 Univariate distributions for continuous variables

5.0.2 Univariate distributions for categorical variables

5.0.3 Interactive graphics

5.0.4 Pairwise associations

6 Data screening

6.0.1 Missing data patterns

7 Initial data reporting

7.0.1 Creating tables

8 Refining/Updating the analysis plan

9 Reporting IDA findings in the final report/manuscript

10 Session info

11 References

Initial Data Analysis

Center for Statistical Training and Consulting1

11 April 2020

1 Housekeeping

2 Data set

2.0.1 Sociodemographic variables

2.0.2 Health and behavior variables

2.0.3 Physical activity data

3 Framework for a systematic initial data analysis (IDA)

3.0.1 Intended statistical analysis

3.0.2 Six elements of IDA

4 Meta data

5 Data cleaning

Data Frame Summary

Data Frame Summary

5.0.1 Univariate distributions for continuous variables

5.0.2 Univariate distributions for categorical variables

5.0.3 Interactive graphics

5.0.4 Pairwise associations

6 Data screening

6.0.1 Missing data patterns

7 Initial data reporting

7.0.1 Creating tables

8 Refining/Updating the analysis plan

9 Reporting IDA findings in the final report/manuscript

10 Session info

11 References

Center for Statistical Training and Consulting¹